Development of Auto-Detection System for High Brightness LED Surface Defect with Machine Vision.

• Main Authors: Hsien-Feng Chiu, 邱顯峯
• Other Authors: Yi-Hung Liu
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/71640108102782803302

碩士 === 中原大學 === 機械工程研究所 === 100 === For current Light Emitting Diode (LED) manufacturer, one of the most important goals is to enhance the yield rate and reduce the production cost. To enhance the yield rate, most companies have set up the inspection departments to manually perform the task of defect classification. A fully automatic inspection system is required in order to reduce the effects due to human factors, to accelerate the speed of processing, and to achieve the goal of a full inspection. The purpose of this study is to provide an automatic defect recognition system. In this study, we consult theories of digital image processing techniques. We want our system to automatically classify defect images shot by inspection machines, with the intension of increasing the yield rate of products. We devised a “Defect Recognition System for High Brightness Light Emitting Diode” in the study. This system is able to automatically classify four common defect images, providing a real-time automatic defect detection and classification. The above-mentioned defect images are all offered by a panel company. The experimental results show that, among the 1549 chip pictures offered by a listed panel company in Taiwan, the “Defect Recognition System for High Brightness Light Emitting Diode” achieves a recognition rate higher than 98%. This means that our system is able to classify the four defect images above promptly. Moreover the developed system is also to classify one chip image within 30 milliseconds, which means that the goal of high-speed defect inspection is achieved.